Quantitative Research Methods 1 (DL)

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Apply statistical methods to the analysis of datasets from the Social Sciences, including descriptive analysis and inferential analysis, up to bivariate analysis
• Demonstrate knowledge and understanding of quantitative data analysis, including hypothesis setting and testing, contingency tables, correlation and confidence intervals.
• Use written communication skills developed through the critical interpretation and discussion of statistical results using relevant academic literature
• Use skills in statistical computing, handling and manipulating large datasets and using statistical software (SPSS) effectively.

The module will include online lectures and practice exercises on the following topics: principles of statistical analysis and levels of measurement; introduction to SPSS, data entry, and data manipulation; descriptive statistics, normal distribution, and z-scores; confidence intervals for means and proportions, hypothesis setting and testing, and power calculations; t-tests, ANOVA, and non-parametric tests; chi-square tests and correlation coefficients.

The teaching methods involve lectures, exercises, and tutorials. Learning activities include studying by listening to lectures and attending online tutorials; dialogue on the Discussion Board on Blackboard; practice exercises; and independent study. Students are expected to carry out some reading in advance of each session and also to complete the practice exercises after each session. This helps reaffirm student learning and acts as a self-check as to the level of understanding reached.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.